Human ECG Authentication with Computational Support Vector Machines

Electrocardiograms are unique to each individual and vary depending on their physiological state. In this study, we harness ECG data to identify diseases and distinguish between different individuals. The process begins with the collection of ECG signals from various individuals under different conditions. Next, we preprocess the data using statistical methods to ensure its quality and consistency. Following this, we employ a Support Vector Machine to train on the preprocessed value, enabling us to classify each person’s ECG into distinct categories. Finally, when new ECG data is presented, the trained SVM can accurately identify and classify it, thanks to the robust statistical preprocessing. This approach not only enhances the classifier’s accuracy but also its resilience to variations in individual ECG signals.